This article presents a survey of models of rough neurocomputing that have their roots in rough set theory. Historically, rough neurocomputing has three main threads: training set production, calculus of granules, and interval analysis. This form of neurocomputing gains its inspiration from the work of Pawlak on rough set philosophy as a basis for machine learning and from work on data mining a...

We study multigranulation decision-theoretic rough sets in incomplete information systems. Based on Bayesian decision procedure, we propose the notions of weighted mean multigranulation decision-theoretic rough sets, optimistic multigranulation decision-theoretic rough sets, and pessimistic multigranulation decision-theoretic rough sets in an incomplete information system. We investigate the re...

Nowadays, we need much time and effort for extracting useful information in a flood of data, which are generated everyday by the development of a computer. In order to solve such a problem, many approaches are proposed. Data Mining is the whole process for knowledge discovery by analyzing data, or by extracting patterns in specific categories from data. As a Data Mining approach, in recent days...

By means of Dubois and Prade’s idea, we introduce the concept of rough fuzzy normal subgroups and investigate some related properties. Combining rough sets and soft sets, rough soft normal subgroups with respect to a normal subgroup of groups in Pawlak approximation spaces are introduced. Moreover, we investigate the relationships between lower and upper rough soft normal subgroups with respect...

We introduce a system of cooperating intelligent agents acting as an adaptive controller for synthesis of complex objects from elementary parts and transforming a semantic input (a constraint on the nal assembly) into the assembly with a decision attribute (quality) value. The input-output transition is eeected by logic of rough mereology , an extension of the Lesniewski mereology.

We extend the work of T. Lyons [Lyo98] and T. Lyons and Z. Qian [LQ02] to define integrals and solutions of differential equations along product of p and q rough paths, with 1/p+1/q > 1. We use this to write an Itô formula at the level of rough paths, and to see that any rough path can always be interpreted as a product of a p-geometric rough path and a p/2-geometric rough path.

Rough set data analysis is one of the main application techniques arising from rough set theory. In this paper we introduce a concept of inclusion degree into rough set theory and establish several important relationships between the inclusion degree and measures on rough set data analysis. It is shown that the measures on rough set data analysis can be reduced to the inclusion degree. 2002 Pub...

Rough sets have traditionally been applied to decision (classification) problems. We suggest that rough sets are even better suited for reasoning. It has already been shown that rough sets can be applied for reasoning about knowledge. In this preliminary paper, we show how rough sets provide a convenient framework for uncertainty reasoning. This discussion not only presents a new topic for futu...

The collection of the rough set pairs of an approximation (U, R) can be made into a Stone algebra by defining two binary operators and one unary operator on the pairs. By introducing a more unary operator, one can get a regular double Stone algebra to describe the rough set pairs of an approximation space. Sequent calculi corresponding to the rough alg...